Deep Learning Projects – Handwritten Digit Recognition Using Neural Network

Handwritten Digit Recognition Course Overview

The Handwritten Digit Recognition Course offers a practical introduction to deep learning, neural networks and computer vision. Instead of focusing only on theory, you will complete a working AI project that identifies handwritten numbers from image data.

You will learn how to use Google Colab, import essential Python packages and prepare image datasets for model training. The course guides you through building, training and evaluating both a basic neural network and a convolutional neural network. You will also test your models, generate predictions and explore ways to improve their accuracy.

This practical deep learning project course explains each stage clearly, helping you understand why the code works rather than simply copying it. By completing a handwritten digit recognition project using neural networks, you will develop valuable skills relevant to machine learning and computer vision.

Join Cambridge Open Academy and complete an impressive AI project for your professional portfolio.

Learning Outcomes

By completing this Handwritten Digit Recognition Course, you will be able to:

• Explain how neural networks identify patterns in image data
• Use Google Colab to develop and run deep learning models
• Import the necessary Python packages and image datasets
• Prepare and preprocess data for model training
• Build a neural network for handwritten digit classification
• Create a convolutional neural network for image recognition
• Train and test models to improve prediction accuracy
• Evaluate model performance and identify prediction errors
• Complete a practical deep learning project from start to finish

What You’ll Gain

• Understand the project requirements
  Explore the purpose, structure and real-world applications of handwritten digit recognition before beginning your practical work.
• Use Google Colab confidently
  Learn how to create and run a Google Colab deep learning project without installing additional software on your device.
• Import Python packages and data
  Load the required libraries and handwritten digit images to prepare your development environment.
• Preprocess image data
  Clean, organise and transform raw data into a suitable format for neural network model training.
• Build a neural network model
  Create a working model that can learn patterns and classify handwritten numbers accurately.
• Train models and generate predictions
  Train your neural network, test it with unseen images and evaluate how accurately it recognises handwritten digits.
• Create a convolutional neural network
  Build a CNN model and understand why convolutional neural networks are effective for image classification.
• Perform CNN model predictions
  Use your trained CNN to classify handwritten digits and review its prediction results.
• Follow a complete deep learning workflow
  Progress from importing raw data to creating, training, testing and evaluating a functional AI model.
• Develop practical computer vision skills
  Gain experience with techniques relevant to image recognition, classification and other computer vision projects.
• Complete a portfolio-ready project
  Finish a practical MNIST digit recognition project that demonstrates your growing neural network and deep learning abilities.
• Earn a free digital certificate
  Receive a digital certificate after completing the course to demonstrate your newly developed skills.